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    "# 8.神经网络(深度学习)\n",
    "<P>这里仅讨论用于分类核回归的多层感知机(Multilayerperceptron,MLP)前馈神经网络.MLP简称为神经网络</P>\n",
    "\n",
    "## 1.神经网络模型"
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   "source": [
    "# 逻辑回归的可视化\n",
    "import mglearn\n",
    "display(mglearn.plots.plot_logistic_regression_graph()) # 可视化逻辑回归模型"
   ]
  },
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   "cell_type": "markdown",
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    "> 上图为逻辑回归模型,其输入特征和预测结果是节点，系数是节点之间的连线"
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   "cell_type": "code",
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    "# 单隐层的多层感知机\n",
    "display(mglearn.plots.plot_single_hidden_layer_graph())"
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    "> 在MLP中,多次重复计算加权求和的过程，首先计算代表中间过程的隐单元，然后在计算这些隐单元的加权求和并获得最终结果\n",
    "\n",
    "> 每个输入与隐单元之间有一个系数，并且每个隐单元与输出之间亦有一个系数\n",
    "\n",
    "<P>为了让MLP模型比普通线性模型更加强大，我们需要用到一个技巧。即在计算完每个隐藏单元的加权求和之后，对结果在应用一个非线性函数<br>。\n",
    "然后将这个函数的求和结果用于加权求和，计算得到输出y</P>\n",
    "<P>这样的非线性函数有两个:①校正非曲线(reln)和②正切双曲线(tanh)</P>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化两种非线性函数\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "line = np.linspace(-3,3,100)\n",
    "plt.plot(line,np.tanh(line),label='tanh')\n",
    "plt.plot(line,np.maximum(line,0),label='relu')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel(\"relu(x),tanh(x)\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 可看出：对于tanh,在输入值较小的时，取值接近-1.输入值较大时接近1.<br>\n",
    "  对于relu,他会截断小于0的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
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      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 两个隐层的多层感知机\n",
    "mglearn.plots.plot_two_hidden_layer_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.神经网络的调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature 1')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.datasets import make_moons\n",
    "from sklearn.model_selection import train_test_split\n",
    "X,y = make_moons(n_samples=100,noise=0.25,random_state=3)\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=42)\n",
    "mlp = MLPClassifier(solver='lbfgs',random_state=0).fit(X_train,y_train)\n",
    "mglearn.plots.plot_2d_separator(mlp,X_train,fill=True,alpha=0.3)\n",
    "mglearn.discrete_scatter(X_train[:,0],X_train[:,1],y_train)\n",
    "plt.xlabel(\"Feature 0\")\n",
    "plt.ylabel(\"Feature 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<P>由图可知:决策边界完全非线性,但相对平滑。默认情况下MLP使用100个隐藏点</P>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'F 1')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aqT2STmYo1lEw43hljUMaddWrKyqw+WY/rlxbjnmTJqNX9+6eDX8Qb2AkpKH12XYbLDe3AEU5EPfwWhGYKSufu2wJcGgzHs9P6TQU18pUPULMEGusWeuzLSN65eYUaw9jNK9aqanBBdOnYe/MVORmJEFpPIa+K1qxZvYSnJqRTbEmjiBiI1Drs7131QtSums9sWYYxKOYKYB5tHTtSb2Dp/RNxprtr2BSobh+JIToYUWWRqTP9tR+yRH3ZWSHYu1BzGwqBuN5e2emdvp94ZAknL9iO0bXfee67mbEHVidQqf12Z43KAl9V7gvdk2x9iixuuogXusdLAOKYm2rXZlxIrdZ77PtRndNsfYYIlx1kESfzCGSRJsK43ThSbTPthvdNcVaIIpi3DmZOdYs8brqIHTXYlAU70+FcVqcwzHy2Xabu6ZYC8KMc7LKZZktK3/744+w8+MjWOSy3sFuI9ZujLIimzBHwuhne8h5e+25IAEwdU8AimJ8LBeAjmOTumaj8d3X0fzRNhwLNCDJn4lTzuqHti/fw9LH3zLtsuJtgcqcamsIHSGmRWudgsOlhXhxzR4br8wYbhBnK9Bqv2AlnkzdUxR5NmvMOCdVVeHPy8exH5vx3SsPI73/aPSa8hhSjrvrpj0VCPzzKJ559o948IFVhq9BhFATa3DbVJhEFedw9NovOEFMYu3z+UapqrpJ9MUYRbbNGjN9rFVVRfqYAvywoQinT3zwpBhmzrBp6HrOQLyz7gEoygFbbzx01dYg81QYCnNkZCxRj3Vg7rNCr8IEinJisyZz6FSk5uTCl5SM1JxcZA6diuzx96OoeLbmcForCB2+qkXQOQWa6hH47O9I7z9a14mnX3iV4QG5DH/IzRXDJiBQre9t7JoKI3qIq1cJblBelJvcsRHpNJpi7fP5XtX4eQ3AqTZeYyecmAQejaBz0iPonPzp2Wj+aBvS++XrHp9x0RhDk63j7VXN8If1ODG9PQjF2TxBVz1vULs8zhuUhNUV5fi21tnvil4Y5HIAUwA0hf3eB2CgZVcUBTsngRvFTB9rVVXxxsZVhp24EeL9wsnsqmtrD+GJx2dh9l1PuTbX265ujAxpiEHWEnU9sf47gCOqqm4Lf8Hn831i3SXpI+Nmjdk+1m++tUZIDDMRXHXZy4uw/9Pdrs/1tmK4BMVZPDKXqGuKtaqqV+m8NtSay4mOjJs1Zp3TJQOuwMfvv4Huw2/TXNNoDDPWL6cbpr/U1h7ClsqXsOVmP4avtbaSUlGszy6Kd7gExdl6ZC5Rd13qnh2TwGPBjHO6ffoC/PtdV6Pll4NtnWy9/+BBlKwvw9rKLWhobJC+N0XZy4s6Nnmm9YNl7lq27CKAwuwEspeou64oRlGMF6DIKEBBTghEZCc+49YHsP+f+zSdntkMkDd378aNfylCWr/RSOub35HXHajehEB1hXS9KWprD2HOrMHYNzOlow/x+SvasOTJXYbdtZF4t6LI8XmiODtP6JACzWMsHl7gueED0YRONuHRQlEiT5T5xdnn4enn/gR/Xj78eaMiCuvVXboY/jLvP3gQF945G+njH3TNDW7VyntxVs0rWJyf3PG7ORVH8WWP6w2761Ur78W2zWsw7MqbNd+zfOUC7KppRKbOk1rD9tUY3CPL8tmYbhfn8Ce3zIxMTB4xHHdNGI8+Z5zh9OVF5bLZM7Hz4/1RjxtyXh/sWLrckmuISax9Pt9Zqqp+GcsJ7Sg3VxQ5RmeJRlGiO73Df3sYa2Y+jOuu+pWhNe9c9iTWfZeC9MunaR5jhSDFSrirDmLGXQfX2DIlGcPXHtV8jx2l4PEIs1sEUOvJreXDCrTsKcdL987HVQMdSyJzDbGK9buqql58/M+vqKo60egJE603iEgMOb1tz+O3ucfwxKzfG1oze+IN6DbpMdf0pojkqoMYddeha+i9Z9y43uh993r4kk4+VxD1aBu+KpmIDWXGvIso1+wWATTy5Nbwt4dwyb+ciXUL/uh4JaDM6Im1XgWjL+TPvxB7SUSLrdvWw583SvcYf7/RWLN5i+E1GxobhOZ1W0kwA6RwkC/i64WDfNhSuQ51ddpFSOFr6L3HTEGTFlYUnuw/eBA3/qUI6eMfRPrl0zpV6qZfPg3p4x/EjX8pwv6DB02vLZqS9WXtNxSdQrWU7NOxZ/+nUlQCuhU9sVY1/kwsxGjpemPTYcNrZmZkxi1IdhHMADHSY9voGnrviaUUPFycw4VZROzZiACm5eXj8bINcZ8rXtZWbkFaX+2K3LamWrTWfIXKqV2lqAR0K3pi3d/n8zX4fL5GAP2O/7nB5/M1+ny+BrsuMNEw6vQy0rMMrzl5xHD8uKdC9xgn0h0j8flnVViyqxm+hxo0f5bsasbnn0busa3lzLXctZFS8B8/KMf0c/M0xdkKogkgAKTlmXvCsopoT24//qMUt1yYKlWfDTeiVxSjHcQjlmEkj7yluhxTRg43vObIX1yBVf/zH+hyzkBb87pj4c+P6d9UoqHlzLWm3ugWNO0px497KvDK/fNx1UBjm7miaGhsQJbgJyyrCD65RdoTaWuqReDD/8GDs7oAcD5X2c3E2nWPWIQRp9dSXYG548cZWm/37lr87NRemF+wFPVlC9GwfTVa6xSoR9vQWqegYftq1JctFNKbwmliiXf3rN6Bq7t0wdrfPYSRviNoWHsPvi65Ds2l8/Db3GOofmqpI5t4RkNXZp6wrGLyiOFo+TDyTfbHf5Tilv4pEftsEHO4Ms/a62jmke8pR+uHFaayAELbnyqKN9Mdg+hlkQSZ81Ybfki+DHPGtJf6y5rbbCTdsmn785jUy3hWkFVoZYO0NdWi7tnb8fmsLielYPZd0Yq9q16guw7Dc0UxiYCidBbWbv4MTMsfgbnjxxnOr3VD/w+R3HdPPj78JPpMPSuLGkRhJB2uqexhvL9sqRT51h1phnn5SMsb3R6iKV+ESafswpNXp510vNWVgG6FYu1yQtPBzJBoQwW8VhUYSQDbGr5HS3U5WqrNPWHZwf6DB/F42Qas2bwFDY31OCUZ2P/v3SJm9tBdR4Zi7XJimQTjdaH2mjBrESqAjU2HkZGehSkjh5t6wrIbpaYGQ+fcieE/O4KV10RuigTQXUfCkwNzgyiKPINzZcENfarNEos4OzGdWjR9zjgDT8z6veNxaTM8WroWPxyuxeofVDxdFdA9dsh50cNWpB1Xi7WMrS1FE+t8Rbe7ahHOWbbp1IlAsM1o5dSuuHJtG8McAnGtWCvKicG54RPCU4dORVqfS1FUPFuqTnJ24EZXbUVIQ8bp1IlA50GzKm+UAnFtnrWMg3NFY3Zsl1uyP+wY4irjdGqvI+ugWa/gWrE21PAoL9/QhHCZMStcMgq13RO2KRrOoDdolsSPa8MgMg7OFUmsrtppZMjSeLR0La4/Pwm3bgjg+fF+KebneR2ZB816Bdc6axGtLWVn4MDuUGpq8Jv5BYZcoROu2m7XHI2gaEBtw+5vjuLRnT8BSCx3beYzIwojg2ZJfLhWrGNpbelGQjMatLAzp1o2cQ4n6Kpf/qgVm6d2w+oPWvFt07GEEg0jnxmRhIedwkmkG6WVuFasjTQ8ClRvwthrbrX5yuInKIKhGQ1aH3Yrwx/hwmxV72ZRhLrqaf27HN9cTJXGXe8/eBB3LnsS2RNvQFL+aGRPvAF3LntS6AABI58Z0Wi56iCJdKO0EtfGrHVbW4YMznVr2t7Agd0xd9kSQ2lQoly1DPHmeOhw1XtbsHdWOgBg3pAu6PtUM+YN6eJo7Dp0RFe3SY8h63hNwLoPK/DCnbOFlY47kTr39scfYefHR7Bol/5xLICJD9eXmyuKtzrJBQWzdx8VF0yfhr0zU5GbkRSxl0K8qXpuF+dwLps9E29/th+3XZSKJ8f4O34/a2MAz77Xip+Otv/dykZOkQbcXvPrS7H+f/+OzAkLLG3KpNTURP3MmFnL7dWfboS9QVxEMNQwd9kS4NBmPJ5/4uEnvJeCmVi114Q5EuFi1fF7m5oGrXrzTcx8cjn8/UYj/cKrOwbc1rz+X0j72a+QM/w2zfeKaHdq5DNjZq3V5Rtxy2+uYQaNjcQ6MJc4hNaGTWjMNZpQy74RaAVOZiSsevNN3L50GU69/iHkDL+t04Db1vpDSL/wat33xzuiy8hnxuxadsa9SXQo1hIRdNXRRKdg8bMR35to4hyKkYyE58vfskR49h88iJnLliPj4msjhjmOBYxNl49nRJfIGxWrP+WEYi0ZRkTnjfe2oqax3jVZGnZgJCNhcl+fJcJTsr4MRwGk9x8d8fUkv7UjukSmzrH6U14o1pIQdMPG0qBSsOmTjQkrzJF4++OPsGjXEd2p6E/sbsHOD/cIP/fayi049lNA0z13+9UwNEWZLq83BDlakYvI1DmWjMuLa1P3vMjAgd1x11+ZBhULWtkdoZtu7Ztt/YSfu6GxocM9R5rwnXHxNfj2rwXoqjNdvqW6AnNnLo24frRWr6JS51gyLjfMBpGAWMd2EX1EprLpkT3xBrSddQmS03OQMyzygNvA/nfw/auPIuui36Br/6sMj+gK/jdsnpJieX/oSNkkHa9xqostMBvEBbhFqJ3oOxErdj3STx4xHKldUtH0QblmRW3SKd2QjGO4JqsZzaXz8HXJdWgunYdJvY7h/WVLNQti7NrsY8m4/FCsiSns7jsRKyJT2aJx14TxaPt0J7L+9QZ898rDqNv2PFrrFKhH29Bap6Bu63M4tO4BrPi/s/DXe+5G/Ssv4Wh5OepfeQlPzPq9ZiGMnZt9LBmXH8asHcZsK1QnCFazFf1ulmumrxhJZRP1SN/njDPw0r3zceNfipB53mC0Nh/Gt2vuwbFAA5K6+JEMFc/MvhPTr7oqrv8GK8vlWTIuP4xZO0ysMxbtJFjNds6ZZ2HIad+FbNbJGcPUqmTseN2i2LXISeROV2MSZ2DM2gYU5QCWr1yASZPzMG5cb0yanIflKxdAUQ5ovsctrjropvd+cQC3HE+mkDmG6dQjfXASudEwhx4y9YeOtE/hpr0Lr0CxFkBVVSXmFIzBrppGZN9UhN53r0f2TUXYVdOIOQVjUFVVqfle2V116AbX9AtTsfqDNgByxzCN5Fwv2nUEu/fJ+Ugv22ZfpH0Kt+xdeAmGQeJEUQ5gTsGYk6asB2n5Zh/qyxZGnLIuewgkYurbU83YO6sbeqVblwqXCOh1tdNLoes4xqYwVKTUQVVVbUsnTDQYBrGQWKesyy7UgNYG14lm/jK7a9nRc6YyPRlESh1k7xBnoLOOk0mT85B9U1HEyrUgrXUKDpcW4sU1J0qdZRdr3Q0uG9y1l/sp21noEg+RnqwuWP4TjqnAvt93sbTQKFGhs7aQQFO9oY5qoVPW3bCxqL/BZb279nJM1C3ONNKT1eS+Pvyy+zH2DnEAinWcxDplXXZXrbvBNaQLlr/zk2WP5F7up+yWrnZan4H7LkvF5zVt+LbpWMfvtP4bmDEiFop1nJidsu5mVx0kNyMJd1zaFXMnjIW6aRPUTZuEjslyi/OMBbd0tdN7sprWv0vHk1Xwd5H+G7z8dOQEFOs4iWXKusyuGnB2g8sO5+mU47OzBD4ejDxZrf6gVddde/npyCko1nESnLJeX7YQDdtXd+oJ0bB9NerLFrpuyvqOpcs7HLPejxVDZ+1wnk45PpkKXfQwVlSUquuuvfx05BTMBhGEokSfsi57BojT2FFi7VQmhlMl8LFw2eyZ2Pnx/qjHdU0FjrR2/t2Q8/rgv/9zoS2tab0Ip5tLAsVaHzv6KZ88jODEmlamC8pU6GI1IqesJxpM3ZMAN2wsOokdJdbR4uFWhkdkKnSxErfE5d0IW6TaCF21NmaaL8XqzvTi4ff8228tbf9qRXxfRuxsTZtoUKyJFFjdTznafMGmQCBkQ0ylqMSA1r9xEFGzHL1c3aoHxdoGGKuOjtXOU8/x3ZyXjFVbKvHJnX4A3hsQa5e42fF0FDyP3gBhr8KYNfE80eLh8wcnAepR+Hztf5ctlS5e7EpVtCMun8j523TWFsONRecx4vimX9SeN1wy+hQA3nHXoeJm9Sg2O+LynfO3EytcRbG2AYZAnMVwPLx3csefvbIh5iVxC4+Je+WGatfa+B8AAAdFSURBVBSGQSyErto+9ErItSoyD5aWIqdrGg7elQ71PzKxY3q3Tu9ze7qZW5pGGcUtfVWsgmJtMXTV9hBLXNapWY124SVxY/42wyDEA8Qal7U6XdBJoqUqui10wPxtirVlMF3PPmKNy3q5UMVL4mZX/rbsMAxCLMHqNqTB9T/Yv99TcVkRyDYdPV68Hq4yCsXaArixaH1ub3D9Gf/1iLRxWaf6ZntN3BKlr0o0GAaxiEQOgVid2xu6/mXPHcDTI7t2el2Wx2I7K+1CqxS9Fov3crjKDBRrwdBVW5/bG7r+9AtTsfqDNpT0OvFRliEua2cxCtD5xkBx8yaeCYMoygEsX7kAkybnYdy43pg0OQ/LVy6Aohyw/Vroqq2LIYevf9/laSeNmLLivGaxc1JKIpdgJxKeEOuqqkrMKRiDXTWNyL6pCL3vXo/sm4qwq6YRcwrGoKqq0ulLTBiszu2NvH7nEVNWnBc4OQatFZO2uxiFI7QSA9eLtaIcQFHxbGSPvx+ZQ6ciNScXvqRkpObkInPoVGSPvx9FxbNtcdiJHgKxunBBc/0hXbD8nZ8s33QK3zTV2kS1sxjF6I3Bqc1OIg7Xi/WG11bBn5ePtDPPj/h62pnnw583Cq++/pwt15PIIRCrB8LqrX/HpV0xd8JYy4b6hocagimD4aEHuyvtjN4YnBoSTMTherHeum09/HmjdI/x5+Vj67b1ll4HXbW1ub1O5w6HhxqCKYPhoQc7J5gbvTEwpu0NXC/WgaZ6pGSdrntMSuZpONJUb/m10FVbl9vrZO5wpFDD3i8O4JZ+6Ph7qNu264Zi9MbAmLY3cH3qnj89G22Hv0NqTq7mMW0N36NrerZl15Dorhqwvs+Gk7nDkUINoSmDQXEML9AJR2RKodES7Gmjr07otqJewvVifcWwCdhVvQmpQ6dqHhOorsAVwyZYeh2J7KoB6wsXnMod1hLF+y5PQ9+nmjFvSBf0Sk/CvEFJOG/ZF3j7c9WWG4rRJw29Cs97/u23CTnL0K24XqzHXTsdlQVjkNbn0oibjC3f7EOgehPGFm+05Px01d5GP9RwYrpMbkYSpl/sh6/XlbYU4hh90shM+wIbxp7cp7vvinI0//hjQs4ydCuuj1nn5p6N+QVLUV+2EA3bV6O1ToF6tA2tdQoatq9GfdlCzC9Yitzcsy27hkR31V4l6qbmkC6dCnLsSptTamqQ3rUblHXrIg5VCP78Yfy1mH6xP+KNZuJ5Sfh/lZu56egiXC/WADBgwAgsLt6IwT2ycLi0EF+VTMTh0kIM7pGFxcUbMWDACEvOS1ftbYyFGk4U5NiVNmdkvWg3GqhtuDkviZuOLsKnqqrwRc89t79aUvKm8HVlgz2rvUFoE6TQ2O1ls2di58f7o76/aypwpPXE34ec16cjxq7U1OCC6dOweUoKrlzbhr2rXogrPmx0vbnLlgCHNuPx/JMjnUrjMVzwZBP2zkpHbkYSlMZj6LuiNe5rI/HjGzWqSlXVSyK95vqYtVPQVXsHre54IjY1RTe1MrqeXky7SzJw20WpETcdGbuWF0+EQZyCrtr9WFkwIrpHiJn19IYEd0tLw4ND0zod73TjKxIdinUM0FV7BysLRkT3CBGxnp0VlkQsFOsYoat2P1Z2xxPdI0TEek6X7JP4oFibhK7aO1jZHU+0gxWxntfGfSUa3GCMAbpq96NVmSiiHFv0NG5R63lt3FeiQbE2AV21dzDiVGPNjDDjYI2cQ9R6HPflbijWJqGrdj+inW84oh0sHTEBKNaGoav2DqKdbziiHSwdMQEo1qagq/YGdKrEjVCsDcCycm9Bp0rcCFP3CCHEBVCso0BXTQiRAYq1DtxUJITIAsU6CnTVhBAZoFhrQFdNCJEJirUOdNWEEFmgWEeArpoQIhsUaw3oqgkhMkGxDoOumhAiIxTrCNBVE0Jkg2IdAl01IURWKNZh0FUTQmSEYn0cumpCiMxQrEOgqyaEyArFGnTVhBD5oVgfh66aECIzCS/WdNWEEDeQ8GIN0FUTQuQnocWarpoQ4hYSWqwBumpCiDtIWLHmuC5CiJtIWLEmhBA34VNVVfyiPt/3AL4QvjAhhHibn6uqelqkFywRa0IIIWJhGIQQQlwAxZoQQlwAxZoQQlwAxZp4Ep/Pd9Tn870f8nN2hGOm+Xy+z47/TLP/KgkxDjcYiSfx+XxNqqqm67zeHcA7AC4BoAKoAjBAVdU6my6REFPQWZNEZTSATaqq1h4X6E0AfuPwNRGiSYrTF0CIRfh9Pt/7x//8T1VVJ4S9fiaAr0L+/vXx3xEiJRRr4lUCqqpeqPO6L8LvGBMk0sIwCElUvgbQO+TvPwNw0KFrISQq3GAknsTgBmMVgIuP/+pdtG8w1tpxfYSYhWEQkpCoqlrr8/keBvD28V/9kUJNZIbOmhBCXABj1oQQ4gIo1oQQ4gIo1oQQ4gIo1oQQ4gIo1oQQ4gIo1oQQ4gIo1oQQ4gL+P4iADmZEHlxJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用10个隐藏元\n",
    "mlp = MLPClassifier(solver='lbfgs',random_state=0,hidden_layer_sizes=[10])\n",
    "mlp.fit(X_train,y_train)\n",
    "mglearn.plots.plot_2d_separator(mlp,X_train,fill=True,alpha=0.3)\n",
    "mglearn.discrete_scatter(X_train[:,0],X_train[:,1],y_train)\n",
    "plt.xlabel(\"F 0\")\n",
    "plt.ylabel('F 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'F 1')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 添加两个隐层,每层10个单元\n",
    "mlp = MLPClassifier(solver='lbfgs',random_state=0,hidden_layer_sizes=[10,10])\n",
    "mlp.fit(X_train,y_train)\n",
    "mglearn.plots.plot_2d_separator(mlp,X_train,fill=True,alpha=0.3)\n",
    "mglearn.discrete_scatter(X_train[:,0],X_train[:,1],y_train)\n",
    "plt.xlabel(\"F 0\")\n",
    "plt.ylabel('F 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'F 1')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 添加两个隐层,每层10个单元,这次使用tanh非线性\n",
    "mlp = MLPClassifier(solver='lbfgs',random_state=0,hidden_layer_sizes=[10,10],activation='tanh')\n",
    "mlp.fit(X_train,y_train)\n",
    "mglearn.plots.plot_2d_separator(mlp,X_train,fill=True,alpha=0.3)\n",
    "mglearn.discrete_scatter(X_train[:,0],X_train[:,1],y_train)\n",
    "plt.xlabel(\"F 0\")\n",
    "plt.ylabel('F 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 此外可以通过正则化参数alpha的来调节神经网络模型的复杂度\n",
    "fig,axes = plt.subplots(2,4,figsize=(20,8))\n",
    "for axx,n_hidden_nodes in zip(axes,[10,100]):\n",
    "    for ax,alpha in zip(axx,[0.0001,0.01,0.1,1]):\n",
    "        mlp = MLPClassifier(solver='lbfgs',random_state=0,hidden_layer_sizes=[n_hidden_nodes,n_hidden_nodes],alpha=alpha,max_iter=10000)\n",
    "        mlp.fit(X_train,y_train)\n",
    "        mglearn.plots.plot_2d_separator(mlp,X_train,fill=True,alpha=0.3,ax=ax)\n",
    "        mglearn.discrete_scatter(X_train[:,0],X_train[:,1],y_train,ax=ax)\n",
    "        ax.set_title(f'n_hidden=[{n_hidden_nodes},{n_hidden_nodes}] \\n alpha={alpha}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 如果随机种子不固定则每次生成的结果不一样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集:0.9389671361502347\n",
      "测试集:0.916083916083916\n"
     ]
    }
   ],
   "source": [
    "# 使用乳腺癌数据\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "cancer = load_breast_cancer()\n",
    "X_train,X_test,y_train,y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
    "mlp = MLPClassifier(random_state=42)\n",
    "mlp.fit(X_train,y_train)\n",
    "\n",
    "print(f\"训练集:{mlp.score(X_train,y_train)}\")\n",
    "print(f\"测试集:{mlp.score(X_test,y_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<P>MLP的精度相当好,但还赶不上其他模型。这是因为MLP也需要所有输入特征的变化范围相似,最理想的情况是均值为0,方差为1的情况</P>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集:1.0\n",
      "测试集:0.972027972027972\n"
     ]
    }
   ],
   "source": [
    "# 对数据集做标准化处理(即均值为0,方差为)\n",
    "# 计算训练集中每个特征的平均值\n",
    "mean_on_train = X_train.mean(axis=0)\n",
    "# 计算训练集每个特征的方差\n",
    "std_on_train = X_train.std(axis=0)\n",
    "\n",
    "#标准化\n",
    "X_train_scaled=(X_train-mean_on_train) / std_on_train\n",
    "X_test_scaled = (X_test-mean_on_train) / std_on_train\n",
    "\n",
    "mlp = MLPClassifier(random_state=0,max_iter=500)\n",
    "mlp.fit(X_train_scaled,y_train)\n",
    "\n",
    "print(f\"训练集:{mlp.score(X_train_scaled,y_train)}\")\n",
    "print(f\"测试集:{mlp.score(X_test_scaled,y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 可见标准化后效果更好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集:0.9882629107981221\n",
      "测试集:0.972027972027972\n"
     ]
    }
   ],
   "source": [
    "# 增大alpha参数\n",
    "mlp = MLPClassifier(random_state=0,max_iter=500,alpha=1)\n",
    "mlp.fit(X_train_scaled,y_train)\n",
    "\n",
    "print(f\"训练集:{mlp.score(X_train_scaled,y_train)}\")\n",
    "print(f\"测试集:{mlp.score(X_test_scaled,y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析MLP\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.imshow(mlp.coefs_[0],interpolation='none',cmap='viridis')\n",
    "plt.yticks(range(30),cancer.feature_names)\n",
    "plt.xlabel('Columns in weight matrix')\n",
    "plt.ylabel(\"Input feature\")\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MLP(多层感知机)总结\n",
    "<P>神经网络是最先进的模型，只要给足计算条件和时间,它往往能打败其他机器学习算法模型.</P>\n",
    "<P>神经网络的缺点在于:通常需要大量时间训练模型,并且神经模型要求数据的变化范围在同一个范围中</p>\n",
    "<P>神经网络最重要的参数有隐层的个数和每层隐藏单元的个数(hidden_layer_sizes=[层数,每层单元个数])，您应该首先设置一个或2个隐层，然后可以逐步增加,\n",
    "<br>每个隐层的节点数通常与特征个数相同.此外，还可通过alpha参数(正则化)调节模型</P>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
